1. Field of the Invention
The present invention relates to an elevator, and in particular to an improved group management control method for an elevator capable of decreasing an average waiting time and a waiting generation probability by selecting and servicing an optimum car for a passenger, and an improved allocating method for a group management system of an elevator capable of performing allocation and control by considering a current hall call as well as a future hall call, by introducing a genetic algorithm which is known to be highly efficient in a system with a large search space to an allocation algorithm.
2. Description of the Conventional Art
When a call by a passenger is generated in a waiting floor group (hereinafter, called a hall call), a group management system of an elevator evaluates various situations regarding each car's location, operating speed, direction, open/close state of a car door, and a number of passengers, etc., thus allocating an optimum elevator car for a certain situation to the hall call, and servicing the allocated car to the hall call generating floor.
Such a group management system should satisfy various objects such as shortening a waiting time, decreasing an allocation failure probability, that is the elevator car passes without stopping at an allocated floor due to the full capacity of the car, decreasing congestion in the car, reducing a power consumption, etc. In order to achieve the above objects, on the basis of a floor to which a current state of each car and a hall call (a hall call to which an elevator for servicing is already determined) are already allocated, the group management system evaluates a newly generated hall call, and allocates an elevator car which is in an optimum condition for achieving the objects. However, since a transport demand varies momentarily, the group management system may be able to achieve the above objects when properly adapting to a change of the transport demand. Accordingly, the group management system should allocate the elevator car by considering the current hall call as well as a future hall call.
Since such a group management system has limitation in accomplishing a satisfying performance by a traditional controlling operation due to its complexity, an artificial intelligence method such as a fuzzy theory, an artificial neural network theory is introduced thereto.
FIG. 1 is a block diagram illustrating an allocating apparatus of a conventional group management system of an elevator. As shown therein the allocating apparatus of the conventional group management system of an elevator includes a hall button controller 11 for controlling a hall button installed at a passenger waiting floor, a car controller 12 for controlling an operation of an elevator car, and a group management control unit 13.
The group management control unit 13 includes: a information collecting unit 13A for collecting various information from the hall button controller 11 and the car controller 12; a statistics unit 13B for collecting statistics of the collected information; a transport kind characteristic discrimination unit 13C for comparing a current transport state to several predetermined transport kind patterns and selecting a corresponding one; an estimate transport kind generating unit 13F for generating an estimate transport kind; a statistics data base 13E for storing data related with various transport kinds by each class of a time, a date, and a transport kind; an estimate data generating unit 13G for generating various estimate data on the basis of the data stored in the estimate transport kind generating unit 13F and the statistics data base 13E; and an allocating/controlling unit 13D for allocating and controlling the elevator car based from the above information.
The operation of the thusly constructed group management system will be described with reference to FIG. 2 which illustrates an operating state of the elevator.
The information collecting unit 13A obtains data related to passenger information such as a number of embarking/disembarking person by each class of a floor and a direction by applying various sensors installed in each car, and receives a condition of each car (opening/closing of a door, a location of the car, a direction of the car, etc.) from the car controller 12.
The transport kind characteristic discrimination unit 13C compares predetermined characteristics of transport kinds or characteristics of the transport kinds stored in the statistics data base 13E to a current transport kind, and determines which transport kind corresponds to the current transport kind. On the basis of characteristics of the determined transport kind, the allocating/controlling unit 13D becomes able to control the elevator car storing a control algorithm suitable for characteristics of each transport kind.
The statistics unit 13B collects characteristics of current data received from the information collecting unit 13A and the transport kind characteristic discrimination unit 13C by each character of the time, the data, and the transport kind, and continuously renews data in the statistics data base 13E, thus enabling the group management system to properly correspond to the change of the transport kind.
The estimate transport kind generating unit 13F computes information (the number of embarking/disembarking passengers by each floor and direction) of a future transport kind on the basis of the data and the characteristics of the transport kind stored in the statistics data base 13E, and the current transport kind stored in the transport kind characteristic discrimination unit 13C.
The estimate data generating unit 13G generates various estimate data such as an estimate arrival time of the elevator car, an estimate number of passengers using the elevator car, an estimate car stopping probability, a floor at which a car call is generated on the way of a hall call service, etc. based from the future transport kind and the current state of the elevator car.
The allocating/controlling unit 13D allocates an elevator car on the basis of the current state of the elevator car, a current transport kind, and the estimate data, and performs various controlling operations such as a distributed control, an integrated service control, etc..
The operation of the conventional apparatus will now be described with reference to FIG. 2.
FIG. 2 illustrates various kinds of situations of a building where there are 19 floors and 4 elevator cars.
As shown therein, a hall call of an upward direction is newly generated on a 16th floor while each of the elevator cars is servicing a previously generated hall call, and first and second elevator cars are ascending, and third and fourth elevator cars are descending. In order to make a simple description, supposing that one of the first and second cars is allocated to the hall call on the 16th floor, an estimate hall call generation probability of the upward direction which may be generated at each floor will be shown as FIG. 2.
In the above-described situation, each estimate arrival time of the first and second cars with respect to a hall call at a the floor which is not allocated yet is obtained, thus allocating an elevator car of which an estimate arrival time is faster than the other. Here, the estimate arrival time f(t) can be obtained by the following equation:
f(t)=a time when an elevator car arrives at a hall call generating floor+W* (an estimate hall call generation probability of the upward direction * the time required for each stop of the elevator car) PA1 wherein, W is a weighting factor for determining how many data of the estimate hall call should be used for allocating the elevator car. Here, suppose that W is 0.5.
When the time required for an elevator operation between each floor is 2 seconds, and when the time for each stop of the elevator car is 10 seconds, f1(t), an estimate arrival time of the first car, and f2(t), an estimate arrival time of the second car, are respectively obtained by the following equations. EQU f1(t)=14*2+10*W*(0.4+0.2+0.1+0.1+0.2+0.2+0.5+0.4+0.8+0.6+0.7+0.3+0.5)=28+5* 5=53 seconds EQU f2(t)=8*2+10*W*(0.5+0.4+0.6+0.7+0.3+0.5)=16+5*3.8=35 seconds
The estimate arrival time of the first car to a 16th floor, which is obtained from the above equation is 53 seconds, and the estimate arrival time of the second car to the 16th floor is 35 seconds. Accordingly, a hall call which is not allocated yet is allocated to the second car having the faster estimate arrival time. Of course, in a synthetic evaluation function, the allocation is not carried out only by the estimate arrival time. However, since a method for applying the hall call to the allocation is as same as the above-described method, and an estimate hall call generation probability is predetermined at a certain value and uniformly applied to such an allocation method, several problems are occurred as follows.
A distance between a hall call generated floor and a car takes the greatest part in the allocation. Therefore, in determining a car for servicing, even though the estimate hall call generation probability at each floor is changed, the change may not affect on allocating the second car.
Also, when a first section is from an 8th floor to the 16th floor, and when a second section is from a 2nd floor to a 7th floor, applying the estimate hall call generation probability of the upward direction in the first section to both of the first and second cars means that both of the first and second cars are allocated with respect to all future hall calls, which is logically inconsistent. That is, the estimate hall call generation probability applied to the first car should not be applied to the second car.
In addition, when the second car is allocated to hall calls generated in the first section and the hall call generated at the 16th floor, a time for servicing the hall call at the 16th floor is increased, while a serviceability with respect to the hall call in the first section is improved.
On the other hand, when considering the service for the future hall call, it is more proper for a third car to service the hall calls generated in the second section and for the first car to service the hall call on the 16th floor although the estimate arrival time of the first car is slower than that of the second car, since the estimate hall call generation probability in the second section is smaller than that in the first section. In order to consider the above aspect, a probability of a future generated hall call, that is an estimate hall call generation probability, should be considered, however it is difficult to consider the above-described matters in the conventional apparatus.
Also, after an allocation to the previously generated hall call is determined, an allocation to future hall call should be considered as well. For example, after it is determined that the second car is allocated to all of the estimate hall calls in the first section, and the third car is allocated to the hall call in the second section, it should also be considered which car will be allocated to a newly generated hall call.
In addition, a method for allocating a car by an evaluation function (.phi.) is applied as an algorithm which searches an optimum solution by considering various current and future states of each elevator. Here, the current states of the elevator are a current location of the elevator, an operation direction of the elevator, an operation speed of the elevator, and a number of passengers, and hall call and car call which are previously allocated, etc., and the future states of the elevator are an estimate number of passengers, an estimate arrival time of a car for servicing a hall call, a probability for which the car stops on other floors while servicing to a floor at which a hall call is generated, and a location of the elevator at a predetermined time, etc.. EQU .phi..sub.k =.alpha..sub.1 .multidot.X.sub.1k +.alpha..sub.2 .multidot.X.sub.2k (1)
wherein .phi..sub.k is an evaluation function of a Kth car, .alpha..sub.i is a weight value, and X.sub.1k is an evaluation value of an estimate arrival time with respect to each hall call when considering location and stop probability of the Kth car, and X.sub.2k is an evaluation value obtained by considering congestion of a Kth car and long-term waiting probability of a Kth car.
When a hall call is newly registered, an allocation of the new hall call is evaluated on the basis of the evaluation function (.phi.), and a car having the smallest evaluation value is allocated as a result of the evaluation. However, such a method may not appropriately consider the estimate hall call, thereby being not capable of responsibly adapting to the change of the transport kind.
Accordingly, in order to allocate an optimum car by synthetically considering various future states using the conventional apparatus, the estimate hall call should be evaluated by additionally considering an estimate hall call generation probability which varies dependent upon the above-described situations.
However, to consider the estimate hall call generation probability, an estimate hall call with respect to each floor, an estimate hall call to an operational direction of each car, etc. should additionally be considered, whereby a solution may not be obtained within a predetermined time since computation volume of the conventional apparatus is rapidly increased, and a serviceability of the elevator may not be dropped due to inefficient computation.